Abstract

The similarity of objects is one of the most fundamental concepts in any collection of complex information; similarity, along with techniques for storing and indexing sets of values based on it, is a concept of ever increasing importance as inherently unordered data sets become ever more common. Examples of such datasets include collections of images, multimedia, and semi-structured data. There are however two, largely separate, classes of related research. On the one hand, techniques such as clustering and similarity search give general treatments over sets of data. Results are domain-independent, typically relying only on the existence of an anonymous distance metric over the set in question. On the other hand, results in the domain of similarity measurement are often limited to the context of pairwise comparison over individual objects, and are not typically set in a wider context. Published algorithms are scattered over various demand-led subject areas, including for example bioinformatics, library sciences, and crime detection. Few, if any, of the published algorithms have the distance metric properties. We have identified a distance metric, Ensemble Distance, which we believe can help to bridge this gap. Ensemble Distance is a non-Euclidean distance metric which we believe can be used in the treatment of many classes of structured data. For any complex type where a useful characterisation exists in the form of an ensemble, we can produce a distance metric for that type. This will in turn allow use of the complex type within off-the-shelf clustering and similarity search algorithms; this would be a major result in the management of complex data sets.